Authors:
Adleni Mallek
1
;
Fadoua Drira
1
;
Rim Walha
1
;
Adel M. Alimi
1
and
Frank LeBourgeois
2
Affiliations:
1
University of Sfax, Tunisia
;
2
University of Lyon, France
Keyword(s):
PCANet, Deep Learning, Sparse Coding, Text Detection.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Image Generation Pipeline: Algorithms and Techniques
Abstract:
Text detection in the wild remains a very challenging task in computer vision. According to the state-of-the-art,
no text detector system, robust whatever the circumstances, exists up to date. For instance, the complexity
and the diversity of degradations in natural scenes make traditional text detection methods very limited and
inefficient. Recent studies reveal the performance of texture-based approaches especially including deep models.
Indeed, the main strengthens of these models is the availability of a learning framework coupling feature
extraction and classifier. Therefore, this study focuses on developing a new texture-based approach for text
detection that takes advantage of deep learning models. In particular, we investigate sparse prior in the structure
of PCANet; the convolution neural network known for its simplicity and rapidity and based on a cascaded
principal component analysis (PCA). The added-value of the sparse coding is the representation of each feature
map via coupled dictionaries to migrate from one level-resolution to an adequate lower-resolution. The
specificity of the dictionary is the use of oriented patterns well-suited for textual pattern description. The
experimental study performed on the standard benchmark, ICDAR 2003, proves that the proposed method
achieves very promising results.
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